neural network based clustering using visual features of ... · their work related to optical...

International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS Vol: 11 No: 04 12

118704-5656 IJVIPNS-IJENS © August 2011 IJENS I J E N S

Neural Network based Clustering using Visual

Features of Characters‟ Shape in Image Safdar Zaman, Wolfgang Slany, S. Nadeem Ahsan, Farhan Hyder , Farukh Nadeem

{szaman,wsi, sahsan,fsahito}@ist.tugraz.at1,[email protected]

2

Institute of Software Technology, Graz University of Technology, Austria1

Institute of Broadband Communications, Graz University of Technology, Austria2

Abstract-- Clustering gathers similar objects. A Character

can also be treated as object and can be recognized in the

image through its visual features. In this work, characters of

the Urdu script are clustered on the basis of 18 different visual

features. A Kohonen Self Organizing Map is used for

clustering with four different topologies of sizes 6x5, 8x7, 9x8,

and 10x10. Each topology is checked under 75, 100, 150 and

200 numbers of epochs. 30 Urdu characters make 106 different

shapes due to the four different positions in the word. These

106 shapes are then classified into 53 general classes based on

graphical similarity. The shape of each class comprises features

for its description. Considering only 18 features of each shape,

53 general classes are then grouped into clusters using a

Kohonen Self Organizing Map (K-SOM). The above

mentioned work has been implemented in MATLAB.

Index Term-- Character’s shape, Features, Clustering,

Kohonen-SOM, Topology.

1. INTRODUCTION

Clustering is to group similar patterns. It can be thought of

as an unsupervised learning problem. It is a process of

gathering objects into groups whose members are somehow

similar to each other. A collection of objects is called a

cluster if it contains similar objects such that these objects

are dissimilar to the objects belonging to other collections

[7]. In Figure 1 above, four clusters are shown. Clusters

contain more than one object. Objects within a cluster are

similar if the distance between them is as small as possible.

The distance defines a

similarity criterion among the objects of a particular cluster.

Fig. 1. Clusters

Objects can also be grouped into clusters depending upon

their fitness to descriptive features. Such kind of clustering

can be implemented using methods like Neural Networks.

Neural Networks further have different types like Self-

Organizing Map (SOM), Learning Vector Quantization

(LVQ1, LVQ2) etc. Our work is based on SOM Neural

Network known as KOHONEN Self-Organizing Maps.

2. RELATED WORK

A lot of work is being done on object features in pattern

recognition. Our work treats characters as objects. The work

presented here is in fact related to our earlier work [1] in

which a Self Organizing Map has been used for clustering in

the scenario of text recognition. In this previous work

Zaman along with Hussain and Ayub proposed a

methodology for recognition system of segmented

characters of the Urdu script for Nasakh. This work also

partially included clustering as a sub-phase. K.

Gopalakrishnan, S. Khaitan, A. Manik [2] have coped with

how Kohonen Map (SOM) can be trained to identify and

classify chemical analysis data. They applied a 5x5

Kohonen Map to classify one of 13 chemicals. Their

methodology considers winning neuron in SOM on the basis

of minimum-distance Euclidean criterion as here in our

work. J. Vesanto and E. Alhoniemi [3] evaluated Self

Organizing Map for clustering on parameterized distribution

generated data. They applied SOM clustering on three data

sets. M. Hangarge and B.V.Dhandra [10] also presented

their work related to Optical Character Recognition of

Offline Handwritten script. They consider distinct visual

texture of the text in the image. The scripts they considered

are English, Devnagari and Urdu. Their work extracts 13

spatial spread features of the text, using morphological

filters. These spatial features are then used by KNN

classifier for classification purpose. Prior to feature

extraction they also apply some preprocessing in order to

remove some connected components in the text which may

cause noise. J. Ong and S. S. R. Abidi [4] described in their

work how clusters are the groups of similar objects and Self-

Organizing Kohonen Maps are one of the means used for

data clustering applications. They used Neural Networks as

data mining tool to gain statistical insights of large datasets.

C. Amerijckx, M. Verleysen and P. Thissen, J. D. Legat [5]

in their work, presented a distinguished property of SOM.

They used Kohonen Neural Networks for compression

scheme for the still images. They are also of the opinion that

the KOHONEN ALGORITHM has a number of significant

characteristics besides Vector Quantization: that is, if input

vectors are close to each others in their input space then

their projection in the corresponding output space will also

be close. Kohonen [6] presents a Self-Organizing clustering

algorithm which learns cluster prototypes on low

dimensional grid structure. He considered “features maps”

as a very effective source for pattern recognition. The

Clustering Algorithms Tutorial presented in [7], also

highlights important aspects of clustering. All the surveyed

work clearly shows how important the clustering

methodology is and how efficiently it is used in different

fields. R. Decker in [8] discusses Neural Network towards



in a new dimension. He counts Self-organizing Neural

Network for identifying consumer lifestyles which is an

important characteristic and individual purchasing behavior

of the consumer in the market. He uses vector quantization

as underlying methodology in the algorithm. In his paper he

has introduced a new algorithm which proves its ability to

detect heterogeneous data patterns with a comparatively

small number of parameters to be controlled by the user.

M. H. S. Shahreza and M. S. Shahreza [9] have also worked

on cursive script languages Arabic and Persian (Farsi). They

presented a work with a new method to hide information in

these cursive script languages. The Text Steganography

method in their work, locates suitable letters in the original

text for replacement. Then they change the code of located

letters to hide the information. A. AMIN in [11] presented

his work to cope with the problems related to printed and

handwritten Arabic character recognition. His work also

deals with the segmentation problem of cursive text of

Arabic language.

3. OUR STRATEGY

We use a Kohonen Self-Organizing Map (SOM) for

clustering. Clustering here is used as a separate module in

Optical Character Recognition (OCR). Segmented

characters are clustered on the basis of their features. Figure

2 shows where clustering takes place in our OCR. It is also

shown how Clustering uses features of the character‟s shape

for recognition purpose. The input of the system is image

whose visual features are used by clustering sub phase.

Fig. 2. OCR Process

3.1 CHARACTERS USED

The most commonly used 30 Urdu characters are considered

in our strategy. These 30 characters are not only the most

common in Urdu but 28 of them are also part of the Arabic

character set therefore using them also covers the issue of

clustering in Arabic. The same character set was considered

in our previous work presented in [1]. Figure 3 shows all of

them:

Fig. 3. Used Character Set

Urdu script is a cursive script in its nature, therefore its

words are formed by joining the characters with each other.

A character can assume one of four different shapes

depending upon whether it is totally isolated or joined at the

beginning, middle, or at the end of a word. Because of their

possible position in the word, these 30 characters make 106

different shapes as shown in Table I (seven characters do

not occur at the start or in the middle of a word).

Table I

Of the 106 shapes some shapes are almost identical on the

basis of their main region if their additional regions or dots

are removed. Making the set simpler we combined similar

shapes into 53 classes. This combination of similar shapes is

due to their common features. Every class is represented and

identified by the main region shared by all shapes in its

group. As one class represents many shapes and those

shapes differ from each other by very few features, this

grouping simplifies the classification of a particular shape.

These classes are given below in Table II.

Table II



Every class mentioned in Table II is categorized by 18

features. These 18 features are a feature set for that class.

Each of the 18 features is binary in value. A feature‟s value

is 1 if the class contains that feature, otherwise 0. Features

are given below:

3.2 FEATURES USED

We used 18 visual features for each character. Figure 4

shows some of the features for character :

Fig. 4. Features in Character‟s Shape

Following are some features with their description:

Height: This feature is 1 if and only if the character has a

height greater than its width, e.g., , ,

Width: This feature is 1 if and only if the character has a

width greater than its height, e.g., ,

Loop_M: This feature is 1 if and only if the character

contains a loop in the middle, e.g., ,

Loop_S: This feature is 1 if and only if the character

contains a loop in the beginning, e.g., ,

Cross: This feature is 1 if and only if the character

contains a crossover, the point from which four different

ways exit, e.g., ,

Curve_R: This feature is 1 if and only if the character has a

curve toward its right side, e.g , ,

Curve_U: This feature is 1 if and only if the character has a

curve toward its upside, e.g , ,

Start_H: This feature is 1 if and only if the character has a

horizontal start, e.g., ,

Start_V: This feature is 1 if and only if the character has a

vertical start, e.g., ,

End_H: This feature is 1 if and only if the character has a

horizontal end, e.g., ,

End_V: This features is 1 if and only if the character has a

vertical end, e.g., ,

Endp_1: This feature is 1 if and only if the character has

only one end point, e.g., ,

Endp_2: This feature is 1 if and only if the character has

two end points, e.g., ,

Endp_3: This feature is 1 if and only if the character has 3

end points, e.g., ,

Endp_4: This feature is 1 if and only if the character has 4

end points, e.g., ,

Joint_1: This feature is 1 if and only if the character has

only one joint, e.g., ,


two joints, e.g., ,


only three joints, e.g., ,

3.3 ARCHITECTURE USED

The 53 general classes discussed above are then grouped

into clusters using a Self-Organizing Map (SOM) developed

by Kohonen and shown in Figure 5. Input vector X: 53x18

containing n=18 features for each of m=53 classes, was used

for training the SOM. The topology is a two dimensional

vector which determines the number of clusters. The

topology taken is rectangular but close to square vector.

Fig. 5. Neural Network

3.2 TOPOLOGIES USED

Four different topologies of orders 6x5, 8x7, 9x8, 10x10

were applied. During its learning process, the Kohonen

algorithm started training parameters using Squared

Euclidean Distance. It calculates the distance between input

vector and weight vector and chooses the unit whose weight

vector has the smallest Euclidean distance from the input

vector. The following equation is used to compute the

Euclidean Distance:

For t = 1 to size(Topology) do:

Dt = ∑ (Wit – Xi )2



Where, „Dt‟ is the total distance of Xi in cluster t. „Xi‟ is the

input vector. Wit is weight from input i to output t. Units

update their weights by forming a new weight vector which

is a linear combination of the old weight vector and the

current input vector. The weight update for output unit j is

given as:

Wj(new) = Wj (old) + α [X – Wj (old)]

= α X + (1 - α) Wj (old)

where X is the input vector, Wj is the weight vector for unit

j (jth column of the weight matrix) and α is the learning rate

decreased gradually during learning.

4. RESULTS

Each cluster contains one or more shapes. All shapes of a

cluster share many common features.

With different topologies 6x5 (30 clusters), 8x7 (56

clusters), 9x8 (72 clusters) and 10x10 (100 clusters) we

obtain different results. For each topology we use several

different numbers of epochs, i.e. 75, 100, 150, and 200.

The number of epochs and the maximum possible number

of clusters (topology) are mentioned on the top of each

result table. As we change topology and epochs these

characteristics among the shapes also change.

4.1 RESULTS FOR TOPOLOGY 6X5

Table III shows result after 75 epochs. The topology used is

6x5, i.e. the total number of clusters cannot exceed 30. # in

the table represents the cluster# in the topology grid. The

final number of clusters formed is 23 and all other 7 clusters

contain no character.

Here cluster 1 contains three shapes but cluster 2 has no

shape and so on.

The maximum number of shapes in any cluster is 4.

Table III

(75 Epochs, 6x5 Topology)

Now we change epochs from 30 to 100. Table IV shows the

result after 100 epochs for the same topology 6x5. One can

easily observe the difference from above results. 14 clusters

are obtained after 100 epochs for topology 6x5. The number

of clusters decreases as we increase epochs. The number of

shapes per cluster is also increased. Cluster 1 has no shape.

Cluster 2 gets 5 shapes. Cluster 26 contains all those shapes

which start with a loop and have only one end point.

Table IV


Table V below gives result after 150 epochs for topology

6x5. This gave 12 clusters. Cluster 2 contains the maximum

number of shapes, i.e. 10.

Table V


Table VIII presents result of clustering after 200 Epochs for

the same topology 6x5.



The number of clusters formed is 13. Cluster 9 contains the

maximum number of the shapes.

Table VI (200 Epochs, 6x5 Topology)

4.2 RESULTS FOR TOPOLOGY 8x7 Next the topology is changed to 8x7, i.e. the number of

clusters now cannot exceed 56. The clustering formed 27

clusters after 75 epochs. Table IX shows the clusters.

Table VII


Table VIII below gives result after 100 epochs for topology

8x7. 27 clusters are formed consisting of at most 3 shapes.

Table VIII


Table IX presents result after 150 epochs for topology 8x7.

The total number of the clusters is 23. The maximum

number of shapes in a cluster is 4 while the minimum

number of shapes is 1.

Table IX


Table X shows clusters obtained after 200 epochs for the

same topology 8x7. This time the maximum number of the

shapes obtained is 7 in a cluster.



Table X



Results of clustering for this topology after 75 epochs, is

shown below in Table XI.

Table XI (75 Epochs, 9x8 Topology)

For epochs 100 and topology 9x8, the clustering is shown in

Table XIII below. Total clusters formed are 28. Maximum

number of shapes in a cluster is 3.

Table XII


Table XIII shows the result of clustering after 150 epochs.

The topology used is 9x8. The total number of clusters

formed is 25. Table XIII


Table XIV represents result for epochs 200. The topology

used is the same 9x8.Total number of clusters is 22.

Maximum number of shapes in a cluster is 4.



Table XIV



Table XV below shows the result after 75 epochs. The total

number of clusters formed is 32. The maximum number of

shapes in a cluster is 3.

Table XV (75 Epochs, 10x10 Topology)

Table XVI below shows clusters obtained after 100 epochs

for topology 10x10.

Table XVI


Table XVII presents clusters formed after 150 epochs. The

topology used is 10x10. The total number of clusters formed

is 27. The maximum number of shapes in a cluster is 5.

Table XVII


Table XVIII below describes the results after 200 epochs.

Topology used is 10x10. The total number of clusters is 30.



Table XVIII


5. SIGNIFICANCE

The presented results are of high importance. A clustering is

more useful if it contains almost equal sized clusters with

similar shapes because the final recognition becomes easier.

During classification, an appropriate cluster is found and

then the exact shape is recognized through at most three

extra features. For example using the result (150 Epochs,

100 Clusters) in Table 17, we input to the system an

arbitrary shape „S‟ for recognition, compute its features,

apply them to the system and get cluster #4. As cluster #4

contains four shapes, we look for extra features, i.e., if shape

„S‟ has no dot and has a value of feature Endp_2 equals to 1,

then shape „S‟ is . If „S‟ has one dot and the value of

fetaure Loop_M is equal to 1 then „S‟ is . Hence a shape

can be recognized with less comparisons. This work is also

important because there is no significant OCR work for

Urdu so far and the presented work is of high usefulness in

that direction. Its also notable that any cursive script can

adopt this kind of recognition system in order to facilitate its

OCR system for classification and recognition.

6. OBSERVATIONS

Following is some statistics obtained during the whole

process of clustering. Against each topology and its

corresponding Epoch, there are some findings like number

of clusters, minimum number of characters per cluster and

maximum number of characters per cluster. A topology with

its epoch is given preference if it contains almost equal

number of characters scattered in all the clusters.

Table XIX

Table XIX gives overall findings obtained after the process

of clustering.

Fig. 6. Number of Clusters

Figure 6 gives numbers of clusters formed. It is also

observed that during gradual increase in epochs, there is

generally decrease in numbers of clusters. For example after

least number (75) of epochs:

Topology 6 × 5 gave 23 clusters.






Fig. 7. Number of Characters

Figure 7 shows number of min/max characters found for

different topologies. The number of characters per cluster

also effects the results if there is much difference between

maximum and minimum number of chareacters for a

topology. The size of the character has also an impact

because large size characters take longer for feature

calculations.

7. CONCLUSIONS

In the this paper work, we introduced clustering that

revolves arround the features of the character shapes of

curvise script. Different features help the system place that

character in a specific cluster. Many different clusters are

experimented by changing Topology and Epoch for it. A

cluster conatins all the shapes that share many features

among them. A shape in a cluster can be very easily

recognized with the support of its few additional features.

After this whole process, we can conclude that small

topologies yield a higher number of clusters as compared to

large sized topologies.

As we increase the size of the topology, the number of

maximum characters in one cluster decreases as

shown by the last column of Table 4.

Keeping the same number of epochs, an increase in the

size of the topology increases the number of

clusters.

8. FUTURE WORK

Clustering plays a vital role in character recognition

systems. This work can be extended for recognition of those

cursive scripts (Arabic, Persian, etc.) whose character sets

include shapes discussed in this work. One can also extend

this work by adding more features of the characters to get

better results. The size of the image character can also be

taken into consideration which can increase processing

speed for recognition. One more dimension for future work

could be considering one universal character set which can

cover all those cursive scripts that share many of the

characters among them like Arabic,Udu,Persian, .etc. This

work can also be used as a partial sub phase in the

classification phase of any Offline Optical Character

Recognition System. The proposed work can also be helpful

to the OCR compilers which take Urdu script as input and

digitize it for conversion into script like English, .etc.

ACKNOWLEDGEMENTS

The first author gratefully acknowledges the support from

Higher Education Commission (HEC) of the government of

Pakistan funding his PhD studies at Graz University of

Technology in Austria.

REFERENCES

[1] S.A. Hussain, S. Zaman, M. Ayub, “A Self Organizing Map

Based Urdu Nasakh Character Recognition”, IEEE 5th

International Conference on Emerging Technologies ICET

2009.

[2] K.Gopalakrishnan, S.Khaitan, A.Manik, “Enhanced Clustering

Analysis and Visualization Using Kohonen‟s Self-Organizing

Feature Map”, International Journal of Computational

Intelligence 4;1 2008.

[3] J. Vesanto and E. Alhoniemi, ”Clustering of the Self-Organizing

Map”, IEEE Transactions on Neural Networks Vol. 11, No. 3,

May 2000.

[4] J. Ong and S. S. R. Abidi, ”Data Mining Using Self-Organizing

Kohonen Map: A Technique for Effective Data Clustering &

Visualization” , International Conference on Artificial

Intelligence, (IC-AI‟99), June 28-July 1 1999, Las Vegas.

[5] C.Amerijckx, M.Verleysen, P.Thissen, J.D.Legat,” Image

Compression by Self-Organized Kohonen Map”, IEEE

Transactions on Neural Networks, Vol.9, No.3, MAY 1998.

[6] T. Kohonen, “Self-Organizing Maps”, Springer, Berlin, 3rd

edition, 2001.

[7] http://www.elet.polimi.it/upload/

matteucc/Clustering/tutorial_html/

[8] R. Decker, “A Growing Self-Organizing Neural Network for

Lifestyle Segmentation”, Journal of Data Science 4(2006), 147-

168.

[9] M. H. S. Shahreza, M. S. Shahreza, ” Arabic/Persian Text

Steganography Utilizing Similar Letters with different codes”,

The Arabian Journal for Science and Engineering, Volume35,

Number 1B, April 2010.

[10] M. Hangarge, B.V.Dhandra,” Offline Handwritten Script

Identification in Document Images”, International Journal of

Computer Applications (0975 – 8887), Volume 4 – No.6, July

2010.

[11] A. AMIN,” Off-line Arabic character recognition: The state of

the art”, Pattern Recognition, Vol. 31, No. 5, pp. 517Ð530,

1998.

http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/

http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/

neural network based clustering using visual features of ... · their work related to optical...

Documents